NVIDIA harnesses power of AI to become world’s most-valued company

With a market value of over $3tn, the chip maker is at the forefront of the global embrace of Artificial Intelligence. However, the competition ahead will be fierce.

Nvidia's CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.
Sam Yeh / AFP
Nvidia's CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.

NVIDIA harnesses power of AI to become world’s most-valued company

NVIDIA makes the microchips the world needs to power Artificial Intelligence, and the surge in demand as the technology has spread has made it the world’s most valued company. Its rise to a market value of over $3.333tn has been fast. It came just two months after it reached the $2tn milestone and was driven by its dominance over the advanced graphics processing unit chips, or GPUs, on which AI depends.

The company’s name is blended from the Latin word for envy – invidia – and the acronym NV, which stands for Next Vision. It has been living up to the ambition this sets out.

Strategic financial manoeuvres and continuous product innovation have solidified its leading position in the AI chip market and sent it to the peak of the stock market. It has surpassed the market capitalisation of Microsoft, which is at $3.303tn and in second place. Apple is in third, with $3.222tn. The Middle East’s biggest company, Saudi Aramco, has a market capitalisation of $1.788tn.

Microsoft remains within touching distance of the top spot, and market fluctuations mean it can change hands. While analysts expect NVIDIA to hold on to the top spot, on Monday, 24 June, NVIDIA shares fell by 6.7% due to profit-taking, contributing to a three-day decline of 16% and impacting the broader semiconductor and tech sectors. For example, Taiwan's benchmark index dropped nearly 2% on Monday, marking its largest fall in two months, while blue-chip company TSMC saw a loss of over 3%.

However, share prices could bounce back, especially since NVIDIA announced a stock split to make shares more accessible to individual investors. Historically, such tactics have helped achieve a positive stock performance, boosting investor confidence and growth potential.

Advantages and challenges

Since its inception in 1993, NVIDIA has been highly innovative, and its GPUs have capabilities that mean the company has significantly expanded its market share to become a major force in the global tech industry and a stock market powerhouse. NVIDIA's Compute Unified Device Architecture software system—CUDA—gives it an advantage that its competitors have struggled to match. It has become an industry standard system.

Sam Yeh / AFP
Nvidia's CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.

NVIDIA’s GPUs are renowned for their superior performance. It has brand loyalty in gaming and professional markets, and there is demand for its products from companies of all sizes, from start-ups to multinationals. It is known for its substantial investment in research and development, which has helped it build a reputation for cutting-edge innovation— further boosting its credentials in the fast-moving AI world.

Fierce competition

Nonetheless, NVIDIA is in a fiercely competitive sector. The semiconductor industry contains established rivals such as Advanced Micro Devices (AMD) and Intel. Some of the biggest names in the wider world tech industry have become new entrants in the chip-making sector, including Google, which is developing its own AI semiconductors.

This means that NVIDIA faces constant challenges in its market share. Meanwhile, competition over supply chains, disrupted by geopolitical tension, creates worries over potential product delays and increased costs. NVIDIA’s main AI and gaming markets are volatile, with risks of shifts in consumer demand and vulnerability to technological advancements from competitors.

The company’s growth has also been driven by acquisitions, although it failed in its attempt to buy the major chip designer Arm Holdings, thwarted by regulatory concerns. Such setbacks can impact strategic planning and market confidence.

Blackwell to the future

At a recent major industry event—the GPU Technology Conference (GTC) 2024—NVIDIA CEO Jensen Huang highlighted the transformative impact of AI on systems across industries. NVIDIA introduced its latest GPU architecture, Blackwell, which represents a significant leap in computational power and data management for enterprises.

With 208 billion transistors, Blackwell is designed to handle the vast datasets required for large language models (LLMs). However, transitioning to AI-centric systems involves considerable challenges, particularly the costs associated with establishing the necessary infrastructure. Companies must strategically plan their AI investments to maximise returns.

NVIDIA recently introduced its latest GPU architecture, Blackwell, marking a significant leap in computational power and data management.

Organisations face critical decisions when utilising on-site cloud systems for AI deployment. According to TechTarget's Enterprise Strategy Group (ESG), there's a growing need for on-site systems to leverage data resources quickly. Hardware providers like Dell Technologies, Hitachi Vantara, and Pure Storage are collaborating with NVIDIA to develop AI-optimised infrastructure products and services offering diverse options.

Organisations must define their objectives to integrate AI effectively. This involves considering the extent of data usage, the number of parameters for AI model training, and whether to develop proprietary AI models or use existing ones. Consulting with providers offering a wide range of AI-related products can facilitate informed decision-making.

AI's adoption issues

Managing this change involves significant challenges, including high infrastructure costs, deciding between cloud services and on-site systems, and ensuring alignment between organisational objectives and AI capabilities. NVIDIA's Blackwell architecture offers advanced solutions, yet it is expensive, and its cost can be prohibitive for smaller companies or startups.

A shortage of qualified personnel capable of implementing AI infrastructure makes it difficult for organisations to recruit the right talent. As organisations become more dependent on AI systems that analyse and store sensitive information, ongoing concerns about data privacy and security also need to be addressed.

AI offers numerous benefits—including increased efficiency and the ability to process large datasets quickly—leading to faster decision-making and greater productivity. AI can also enable new services and products, transforming business operations and providing a competitive edge. The computational power provided by architectures like Blackwell can drive advanced research and development, fostering innovation.

However, the initial investment in AI infrastructure and ongoing operational costs can substantially impact company finances. Integrating AI systems into existing workflows can be complex, requiring specialised skills. Over-reliance on AI technology could also expose businesses to risks from technological failures or cyber-attacks.

Sam Yeh / AFP
Nvidia's CEO Jensen Huang delivers his keystone speech ahead of Computex 2024 in Taipei on June 2, 2024.

Hyperscalers seek alternatives

The high costs and short supply of NVIDIA's chips as it has risen to dominate the AI market have encouraged companies to look for alternatives in an industry with a long history of lateral thinking. The history of leveraging GPUs for neural network training began with AI researchers using graphics cards, leading to groundbreaking advancements in image recognition.

Intel and AMD have been actively developing their AI capabilities. While Intel does not dominate the GPU market, it has invested in advanced architectures and software layers to attract data scientists. Its AI Deep Learning Boost and Xeon Scalable processors are part of its efforts to compete with NVIDIA. Intel's latest AI accelerator—Gaudi 3—aims to rival NVIDIA's H100 with superior performance benchmarks.

After acquiring GPU manufacturer Array Technology in 2006, AMD initially struggled to capitalise on the AI boom. However, since 2023, AMD has made a comeback with its MI300X GPU, which features competitive specifications against NVIDIA's offerings. But now, some of the biggest buyers of these chips—large cloud computing services providers like Google, Amazon, and Microsoft, known as hyper scalers—are developing their own custom AI chips. Google's Tensor Processing Units (TPUs), Amazon Web Services (AWS)'s Trainium, and Microsoft's Maia chips, designed in collaboration with OpenAI, are all entering the market. These custom chips offer alternatives to NVIDIA's GPUs, providing competitive performance and cost benefits.

For their part, European companies, including STMicroelectronics and startups like SiPearl and Kalray, are developing AI chips to compete globally. While they face challenges in matching the capital and scale of American giants, these firms focus on niche markets and unique capabilities to establish a foothold in the AI ecosystem.

Watchdogs weigh in

NVIDIA's market position has also attracted regulatory scrutiny. In 2023, the French competition authority raided NVIDIA's offices, suspecting anti-competitive practices in the graphics card sector. While no conclusions were drawn, this highlights the regulatory challenges NVIDIA may face as it continues to dominate the AI chip market.

Both NVIDIA's rapid growth and its dominance in the AI sector have been driven by innovative GPU technology, strategic investments, and strong market demand. Despite facing competition, high infrastructure costs, and regulatory scrutiny, NVIDIA remains a leading force in the AI chip market. As the AI landscape evolves, organisations must navigate these challenges and explore alternative solutions to leverage AI effectively and balance costs, efficiency, and strategic alignment.

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